Source code for transformers.models.clip.processing_clip

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
Image/Text processor class for CLIP
from ...tokenization_utils_base import BatchEncoding
from .feature_extraction_clip import CLIPFeatureExtractor
from .tokenization_clip import CLIPTokenizer

[docs]class CLIPProcessor: r""" Constructs a CLIP processor which wraps a CLIP feature extractor and a CLIP tokenizer into a single processor. :class:`~transformers.CLIPProcessor` offers all the functionalities of :class:`~transformers.CLIPFeatureExtractor` and :class:`~transformers.CLIPTokenizer`. See the :meth:`~transformers.CLIPProcessor.__call__` and :meth:`~transformers.CLIPProcessor.decode` for more information. Args: feature_extractor (:class:`~transformers.CLIPFeatureExtractor`): The feature extractor is a required input. tokenizer (:class:`~transformers.CLIPTokenizer`): The tokenizer is a required input. """ def __init__(self, feature_extractor, tokenizer): if not isinstance(feature_extractor, CLIPFeatureExtractor): raise ValueError( f"`feature_extractor` has to be of type CLIPFeatureExtractor, but is {type(feature_extractor)}" ) if not isinstance(tokenizer, CLIPTokenizer): raise ValueError(f"`tokenizer` has to be of type CLIPTokenizer, but is {type(tokenizer)}") self.feature_extractor = feature_extractor self.tokenizer = tokenizer self.current_processor = self.feature_extractor
[docs] def save_pretrained(self, save_directory): """ Save a CLIP feature extractor object and CLIP tokenizer object to the directory ``save_directory``, so that it can be re-loaded using the :func:`~transformers.CLIPProcessor.from_pretrained` class method. .. note:: This class method is simply calling :meth:`~transformers.PreTrainedFeatureExtractor.save_pretrained` and :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.save_pretrained`. Please refer to the docstrings of the methods above for more information. Args: save_directory (:obj:`str` or :obj:`os.PathLike`): Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist). """ self.feature_extractor.save_pretrained(save_directory) self.tokenizer.save_pretrained(save_directory)
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): r""" Instantiate a :class:`~transformers.CLIPProcessor` from a pretrained CLIP processor. .. note:: This class method is simply calling CLIPFeatureExtractor's :meth:`~transformers.PreTrainedFeatureExtractor.from_pretrained` and CLIPTokenizer's :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.from_pretrained`. Please refer to the docstrings of the methods above for more information. Args: pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`): This can be either: - a string, the `model id` of a pretrained feature_extractor hosted inside a model repo on Valid model ids can be located at the root-level, like ``clip-vit-base-patch32``, or namespaced under a user or organization name, like ``openai/clip-vit-base-patch32``. - a path to a `directory` containing a feature extractor file saved using the :meth:`~transformers.PreTrainedFeatureExtractor.save_pretrained` method, e.g., ``./my_model_directory/``. - a path or url to a saved feature extractor JSON `file`, e.g., ``./my_model_directory/preprocessor_config.json``. **kwargs Additional keyword arguments passed along to both :class:`~transformers.PreTrainedFeatureExtractor` and :class:`~transformers.PreTrainedTokenizer` """ feature_extractor = CLIPFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs) tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs) return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
def __call__(self, text=None, images=None, return_tensors=None, **kwargs): """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the :obj:`text` and :obj:`kwargs` arguments to CLIPTokenizer's :meth:`~transformers.CLIPTokenizer.__call__` if :obj:`text` is not :obj:`None` to encode the text. To prepare the image(s), this method forwards the :obj:`images` and :obj:`kwrags` arguments to CLIPFeatureExtractor's :meth:`~transformers.CLIPFeatureExtractor.__call__` if :obj:`images` is not :obj:`None`. Please refer to the doctsring of the above two methods for more information. Args: text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set :obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences). images (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`, :obj:`List[PIL.Image.Image]`, :obj:`List[np.ndarray]`, :obj:`List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`): If set, will return tensors of a particular framework. Acceptable values are: * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects. * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects. * :obj:`'np'`: Return NumPy :obj:`np.ndarray` objects. * :obj:`'jax'`: Return JAX :obj:`jnp.ndarray` objects. Returns: :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when :obj:`text` is not :obj:`None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when :obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names` and if :obj:`text` is not :obj:`None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when :obj:`images` is not :obj:`None`. """ if text is None and images is None: raise ValueError("You have to specify either text or images. Both cannot be none.") if text is not None: encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs) if images is not None: image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs) if text is not None and images is not None: encoding["pixel_values"] = image_features.pixel_values return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
[docs] def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to CLIPTokenizer's :meth:`~transformers.PreTrainedTokenizer.batch_decode`. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs)
[docs] def decode(self, *args, **kwargs): """ This method forwards all its arguments to CLIPTokenizer's :meth:`~transformers.PreTrainedTokenizer.decode`. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs)